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Evaluation of intensity and color corner detectors for affine invariant salient regions

Nicu Sebe 1 Theo Gevers 1 Sietse Dijkstra 1 Joost van de Weijer 2
2 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : Global features are commonly used to describe the image content. The problem with this approach is that these features cannot capture all parts of the image having different characteristics. Therefore, local computation of image information is necessary. By using salient points to represent local information, more discriminative features can be computed. This research is based on an existing affine invariant local feature detector, in which the features are assumed to be intensity corners. First, the existing algorithm is extended with the intensity based SUSAN corner detector which fundamentally differs from the original Harris corner detector. Second, the algorithm is extended to incorporate color information into the detection process. This results in a comparison between three different detection algorithms: the intensity based algorithm using the Harris or SUSAN detector and a color based algorithm that uses two color extended Harris detectors. The different algorithms are compared in terms of invariance and distinctiveness of the regions and computational complexity.
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Nicu Sebe, Theo Gevers, Sietse Dijkstra, Joost van de Weijer. Evaluation of intensity and color corner detectors for affine invariant salient regions. Computer Vision and Pattern Recognition Workshop (CVPRW '06), Jun 2006, New York, United States. pp.18, ⟨10.1109/CVPRW.2006.75⟩. ⟨inria-00548579⟩



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